Moving target trajectory estimation using Kalman, curve fitting and Anfis methods
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Abstract
Estimation of the possible position of the moving targets after a few steps has great significance especially in terms of defense industry. If a shoot aiming at the target is planned, the issue of estimation of forward position of the target gains importance in terms of accurate strike of the bullet at the target. In target tracking, impact of three different methods as motion estimation method on various motion types has been examined in our study. Motion types have been examined in four different types, which are rectilinear motion, circular motion, sinusoidal motion and curvilinear motion. On the other hand, estimation methods have been examined under three different titles. These are Kalman estimation method, curve fitting method and Anfis method. Different motion types have been examined with different estimation methods and the results obtained have been presented.Â
Keywords: robotics, image processing, trajectory tracking, target tracking, estimation, Anfis, Kalman, curve fitting.
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References
Simon, H. (1996). Adaptive Filter Theory. Upper Saddle River, NJ: Prentice-Hall, Inc.
Jang, J.S. R. (1991). Fuzzy Modeling Using Generalized Neural Networks and Kalman Filter Algorithm, Proc. of the Ninth National Conf. on Artificial Intelligence (AAAI-91), 762-767.
Jang, J.S. R. (1993). ANFIS: Adaptive-Network-based Fuzzy Inference Systems, IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-685.
MATLAB. / Kalman Prediction/Control System Toolbox/Functions/LQR-LQG Design/Kalman.
MATLAB. / FuzzyLogicToolbox/Functions/Advanced Fuzzy Inference Systems/anfis.
MATLAB. / CurveFittingToolbox/Functions/Fit Postprocessing/confint.
Barimani, N., Moshiri B., & Teshnehlab M. (2012). State space modelling and short-term traffic speed prediction using kalman filter based on ANFIS. IACSIT International journal of engineering and technology, 4(2).
Sillen, L. G. (1956). Some graphical methods for determining equilibrium constants II. On curve fitting methods for two-variable data. Acta chemical scandinavica 10. 186-202
Corwin, Jr. et al. (1975). Method and apparatus for programming a computer operated robot arm. United States patent.
Lewis, J. P., Fast Normalized Cross-Correlation, Industrial Light & Magic.
MATLAB/Computer Vision System Toolbox.
http://www.egr.msu.edu/classes/ece480/capstone/spring12/group06/Documents/Application%2 0Note%20-%20Eric%20Mitchell%20%28Servo%20Control%29.pdf?attredirects=0&d=1